{"id":"https://openalex.org/W7160948718","doi":"https://doi.org/10.48550/arxiv.2605.10640","title":"Towards Understanding Continual Factual Knowledge Acquisition of Language Models: From Theory to Algorithm","display_name":"Towards Understanding Continual Factual Knowledge Acquisition of Language Models: From Theory to Algorithm","publication_year":2026,"publication_date":"2026-05-11","ids":{"openalex":"https://openalex.org/W7160948718","doi":"https://doi.org/10.48550/arxiv.2605.10640"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.10640","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.10640","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.10640","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5135945783","display_name":"Haoyu Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Haoyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135956272","display_name":"Yifan Shang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shang, Yifan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135998755","display_name":"Zhongxiang Sun","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sun, Zhongxiang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135980825","display_name":"Weijie Yu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yu, Weijie","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135977299","display_name":"Xiao Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Xiao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5135941677","display_name":"Jun Xu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xu, Jun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.2797999978065491,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.2797999978065491,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.19050000607967377,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.09520000219345093,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/convergence","display_name":"Convergence (economics)","score":0.5748999714851379},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.5727999806404114},{"id":"https://openalex.org/keywords/forgetting","display_name":"Forgetting","score":0.570900022983551},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.39579999446868896},{"id":"https://openalex.org/keywords/dynamics","display_name":"Dynamics (music)","score":0.37779998779296875},{"id":"https://openalex.org/keywords/language-understanding","display_name":"Language understanding","score":0.3301999866962433},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.3224000036716461},{"id":"https://openalex.org/keywords/knowledge-acquisition","display_name":"Knowledge acquisition","score":0.31119999289512634}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7932999730110168},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.5748999714851379},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.5727999806404114},{"id":"https://openalex.org/C7149132","wikidata":"https://www.wikidata.org/wiki/Q1377840","display_name":"Forgetting","level":2,"score":0.570900022983551},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4936000108718872},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.40860000252723694},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.39579999446868896},{"id":"https://openalex.org/C145912823","wikidata":"https://www.wikidata.org/wiki/Q113558","display_name":"Dynamics (music)","level":2,"score":0.37779998779296875},{"id":"https://openalex.org/C2983448237","wikidata":"https://www.wikidata.org/wiki/Q1078276","display_name":"Language understanding","level":2,"score":0.3301999866962433},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.3224000036716461},{"id":"https://openalex.org/C2777220311","wikidata":"https://www.wikidata.org/wiki/Q6423340","display_name":"Knowledge acquisition","level":2,"score":0.31119999289512634},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2953000068664551},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.2802000045776367},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.27320000529289246},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.26739999651908875},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.26739999651908875},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.26350000500679016},{"id":"https://openalex.org/C132758656","wikidata":"https://www.wikidata.org/wiki/Q5307365","display_name":"Dreyfus model of skill acquisition","level":2,"score":0.25429999828338623},{"id":"https://openalex.org/C56814567","wikidata":"https://www.wikidata.org/wiki/Q1323686","display_name":"Explicit knowledge","level":2,"score":0.25110000371932983},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2506999969482422}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.10640","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.10640","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.10640","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.10640","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education","score":0.5150334239006042}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Continual":[0],"Pre-Training":[1],"(CPT)":[2],"is":[3],"essential":[4],"for":[5,72],"enabling":[6],"Language":[7],"Models":[8],"(LMs)":[9],"to":[10,134],"integrate":[11],"new":[12],"knowledge":[13],"without":[14,92],"erasing":[15],"old.":[16],"While":[17],"classical":[18],"CPT":[19,77],"techniques":[20],"like":[21],"data":[22,99,120,137],"replay":[23,100,121,136],"have":[24],"become":[25],"the":[26,29,59,73,87,94],"standard":[27],"paradigm,":[28],"mechanisms":[30],"underlying":[31],"how":[32],"LMs":[33],"acquire":[34],"and":[35,107,144,150],"retain":[36],"facts":[37],"over":[38],"time,":[39],"termed":[40],"as":[41],"continual":[42],"Factual":[43],"Knowledge":[44],"Acquisition":[45],"(cFKA),":[46],"remain":[47],"unclear.":[48],"In":[49],"this":[50],"work,":[51],"we":[52,115],"present":[53],"a":[54,65,69,117],"theoretical":[55],"framework":[56],"that":[57,82,152],"characterizes":[58],"training":[60],"dynamics":[61,106],"of":[62,75,90],"cFKA":[63,156],"using":[64],"single-layer":[66],"Transformer,":[67],"offering":[68],"unified":[70],"explanation":[71],"behavior":[74],"representative":[76],"methods.":[78],"Our":[79],"analysis":[80],"reveals":[81],"regularization-based":[83],"methods":[84,101],"merely":[85],"adjust":[86],"convergence":[88,105],"rate":[89],"parameters":[91],"altering":[93],"inherent":[95],"forgetting":[96],"tendency,":[97],"whereas":[98],"succeed":[102],"in":[103],"shifting":[104],"stabilizing":[108],"pretrained":[109],"knowledge.":[110],"Building":[111],"on":[112,141],"these":[113],"insights,":[114],"propose":[116],"novel":[118],"generative":[119],"approach,":[122],"called":[123],"\\textbf{S}electing":[124],"\\textbf{T}okens":[125],"via":[126],"attenti\\textbf{O}n":[127],"\\textbf{C}ontribution~(STOC),":[128],"which":[129],"identifies":[130],"influential":[131],"factual":[132],"snippets":[133],"guide":[135],"generation.":[138],"Extensive":[139],"experiments":[140],"both":[142],"synthetic":[143],"real-world":[145],"datasets":[146],"validate":[147],"our":[148],"findings":[149],"demonstrate":[151],"STOC":[153],"effectively":[154],"enhances":[155],"by":[157],"mitigating":[158],"catastrophic":[159],"forgetting.":[160]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-13T00:00:00"}